Recent years have seen a growing use of image matching techniques in various application fields. For example, an image matching process analyzes first and second images by comparing local feature values at feature points in the first image with those in the second image and thereby discovering resembling feature points (referred to as “matching points”) in the second picture. The resulting set of matching points is then subjected to statistical processes to detect presence and location of the first image in the second image.
Local feature values may be represented in the form of binary code to facilitate searching for matching points. Binary Robust Independent Elementary Features (BRIEF) is a representative example of such binary code. BRIEF describes local features on the basis of pixel-to-pixel luminance differences calculated for individual pixel pairs placed around a feature point. More specifically, BRIEF uses a set of bit values each corresponding to the sign (i.e., positive or negative) of a luminance difference between paired pixels. Local feature values are thus expressed in binary code form, and this method advantageously allows high-speed evaluation of similarity between feature points using Hamming distances. See, for example, the following documents:
Japanese Laid-open Patent Publication No. 2015-36906
M. Calonder, V. Lepetit, C. Strecha, and P. Fua., “BRIEF: Binary Robust Independent Elementary Features,” In Proceedings of the European Conference on Computer Vision (ECCV), 2010
As described above, each bit of binary code may be calculated based on the sign of luminance differences of pixel pairs. However, this method has the following drawback. Suppose, for example, that some pixel pairs reside in flat and monotonous regions of a picture (e.g., the background area or monochromatic walls). These pixel pairs have almost no luminance difference because their pixels bear a close similarity in brightness. In such regions, the sign of luminance differences is easily reversed by a slight variation of light source intensity, noise disturbance, shooting angles, and the like, even though the imaging device is directed to the same part of the same object. Because of this uncertainty in local feature values, the aforementioned image mapping process would fail to detect in the second picture correct matching points corresponding to feature points in the first picture, thus leading to a poor accuracy of image recognition.